Wavelet-based image denoising using a Markov random field a priorimodel

This paper describes a new method for the suppression of noise in images via the wavelet transform. The method relies on two measures. The first is a classic measure of smoothness of the image and is based on an approximation of the local Holder exponent via the wavelet coefficients. The second, novel measure takes into account geometrical constraints, which are generally valid for natural images. The smoothness measure and the constraints are combined in a Bayesian probabilistic formulation, and are implemented as a Markov random field (MRF) image model. The manipulation of the wavelet coefficients is consequently based on the obtained probabilities. A comparison of quantitative and qualitative results for test images demonstrates the improved noise suppression performance with respect to previous wavelet-based image denoising methods.     

应用卫星电视秒信号传播时延值对同步卫星运动周期的子波分析

讨论了子波变换用于信号突变的检测原理，并用这种方法分析了武昌时辰站１９９４年测量由卫星电视传播并由中央电视台发出的秒信号的时间延迟值，精确检出了该信号的突变点，并由此求出了同步卫星运动周期的变化特征。结果表明：利用子波变换能很好揭示同步卫星的运动特性。     

原子钟相位一时间起伏周期的小波分析

文中根据小波变换的奇异性检测原理，分析了环境温度变化对原子钟特性的影响。基于小波变换的信号重建原理，将温度变化引起原子钟相位-时间起伏进行时-频域分离。用小波变换理论分析了由于昼夜温度变化引起原子钟周期性波动的原因，结合传统的港分析方法，认证了原子钟相位－时间起伏的周期性。结果表明：在有环境温度调节的环境中，氢原子钟的相位－时间起伏标准差41ns左右，在一般环境中，她原子钟的相位一时间起伏标准差21us左右。改善环境条件可以提高原子钟的频率稳定度。     

CCD图像条带噪声消除方法

条带噪声是线阵CCD成像过程中最常见的系统噪声,其存在掩盖了图像的有用信息,给图像判读造成了不利影响。利用小波变换对信号奇异点检测的优越性,检测出条带噪声在小波系数中的周期性分布规律。依据小波去噪三步法,对存在条带噪声的低频和高频小波系数分别进行阈值处理。处理后图像目视效果良好,功率谱基本保留了原图像功率谱的变化趋势,表明在有效去除条带噪声的同时,较好地保留了图像中的有用信息。     

Monitoring of global cerebral ischemia using wavelet entropy rate of change

In this paper, the subband wavelet entropy (SWE) and its time difference are proposed as two quantitative measures for analyzing and segmenting the electroencephalographic (EEG) signals. SWE for EEG subbands, namely Delta, Theta, Alpha, Beta, and Gamma, is calculated and segmented using wavelet analysis. In addition, a time difference entropy measure was calculated because it does not require a baseline and equals to zero in all clinical bands as the initial condition. Visual and quantitative results were obtained from 11 rodents that were subjected to 3, 5, and 7 min of global ischemic brain injury by asphyxic cardiac arrest. We found that the time difference of SWE is capable of amplifying the variations between clinical bands during the various stages of the recovery process and may serve as a novel analytical approach to grade and classify brain rhythms during global ischemic brain injury and recovery.     

一种基于Haar小波变换的低速率拒绝服务攻击检测方法

依据LDoS攻击周期性脉冲突发的特点,提出一种基于Haar小波特征提取的低速率拒绝服务攻击检测方法．该方法采用信号处理技术来分析网络流量提取特征指标,通过小波多尺度分析对网络流量综合诊断,较好地缓解了合法用户背景流量对攻击特征提取的干扰．NS-2仿真实验结果表明,该方法检测率高,消耗计算资源少,具有良好的理论研究和实用价值．     

一种时域质量稳定的三维小波视频编码率控制技术

三维小波视频编码提供了分辨率、时域和质量的可伸缩性。在三维小波编码中，如何调整各小波频带间的码率分配以达到最佳的码率控制至关重要。就小波变换和反变换的结构而言，常规的三维小波视频编码方法会导致解码视频帧沿时间方向播放质量的周期振荡，这种周期的振荡会影响了观看质量。本文研究了重建信号与小波频带间失真分布的关系，提出了一种具有稳定的时域解码质量的率控制方法，实验结果表明该算法在保证播放质量稳定的同时，还具有与常规三维小波视频编码方法近似的平均解码质量。     

基于小波变换技术预测DNA序列的编码区

三周期性是大多数基因组序列的编码区具有的主要特征.本文提出利用小波变换分析DNA序列编码区的三周期性,形成一种新的基于小波变换的DNA序列编码区预测方法,理论和实验研究证实了新方法的可行性,探测率和正确率分别达到81%和75%,特别是探测率较目前常用的其它一些方法有较大改善.     

A study of wavelet entropy measure definition and its application for fault feature pick-up and classification

Shannon entropy in time domain is a measure of signal or system uncertainty.When based on spectrum entropy,Shannon entropy can be taken as a measure of signal or system complexity. Therefore,wavelet analysis based on wavelet entropy measure can signify the complexity of non-steady signal or system in both time and frequency domain.In this paper,in order to meet the requirements of post-analysis on abundant wavelet transform result data and the need of information mergence,the basic definition of wavelet entropy measure is proposed,corresponding algorithms of several wavelet entropies,such as wavelet average entropy,wavelet time-frequency entropy,wavelet distance entropy, etc.are put forward,and the physical meanings of these entropies are analyzed as well.The application principle of wavelet entropy measure in ElectroEncephaloGraphy (EEG) signal analysis,mechanical fault diagnosis,fault detection and classification in power system are analyzed.Finally,take the transmission line fault detection in power system for example,simulations in two different systems,a 10kV automatic blocking and continuous power transmission line and a 500kV Extra High Voltage (EHV) transmission line,are carried out,and the two methods,wavelet entropy and wavelet modulus maxima,are compared,the results show feasibility and application prospect of the six wavelet entro- pies.     

A wavelet visible difference predictor

We describe a model of the human visual system (HVS) based on the wavelet transform. This model is largely based on a previously proposed model, but has a number of modifications that make it more amenable to potential integration into a wavelet based image compression scheme. These modifications include the use of a separable wavelet transform instead of the cortex transform, the application of a wavelet contrast sensitivity function (CSF), and a simplified definition of subband contrast that allows one to predict the noise visibility directly from the wavelet coefficients. Initially, we outline the luminance, frequency, and masking sensitivities of the HVS and discuss how these can be incorporated into the wavelet transform. We then outline a number of limitations of the wavelet transform as a model of the HVS, namely the lack of translational invariance and poor orientation sensitivity. In order to investigate the efficacy of this wavelet based model, a wavelet visible difference predictor (WVDP) is described. The WVDP is then used to predict visible differences between an original and compressed (or noisy) image. Results are presented to emphasize the limitations of commonly used measures of image quality and to demonstrate the performance of the WVDP. The paper concludes with suggestions on how the WVDP can be used to determine a visually optimal quantization strategy for wavelet coefficients and produce a quantitative measure of image quality.     

Analysis of truncated periodic array using two-stage wavelet-packettransformations for impedance matrix compression

A novel method of moments procedure is applied to the problem of scattering by metallic truncated periodic arrays. In such problems, the induced current shows localized behavior within the unit cell and at the same time exhibits cell-to-cell periodicity. In order to select a set of expansion functions that may account for such behavior, a two-stage basis transformation, of which the first stage is an ordinary wavelet transformation performed independently on each unit-cell, has been applied to a pulse basis. The resultant basis functions at the first stage are regrouped and retransformed to reveal the periodicity of their coefficients. Expansion functions are then iteratively selected from this newly constructed basis to form a compressed impedance matrix. The compression ratios obtained in this manner are higher than the compression ratio achieved using a basis constructed via an ordinary single-stage wavelet transformation, where compression is the ratio between the number of nonzero elements in the matrix used to solve the problem and the number of elements in the original matrix. An even higher compression is attained by considering, in addition, functions that reveal array-end related features and iteratively selecting the expansion from an overcomplete dictionary     

Detection and visualization of tandem repeats in DNA sequences

One conspicuous feature of DNA is the extent to which nucleotide subsequences repeat in the genome. Several strongly repetitive tandem (or contiguous) repeats are known to be associated with genetic diseases, while weaker repetitive structures are thought to be representative of historical events associated with sequence repetition. Thus, it is important to develop sensitive and rapid automation of the detection and identification of repeat sequences. A new algorithm for examining periodic patterns in DNA sequences is developed. The algorithm uses the short-time periodicity transform to compute the closest periodic sequence of fixed length at each nucleotide position in a given sequence to be analyzed. Each such subsequence is then compared to its closest periodic sequence to provide a quantitative measure of the amount of repetition within the sequence. In addition to being used to detect the presence of repeat subsequences in DNA, the periodicity explorer algorithm provides a potentially useful visualization of periodic patterns in a DNA sequence through a graphical display of the relative energy in the optimal periodic projections of the analyzed sequences, i.e., the DNA periodogram. Computationally, the algorithm is linear in the length of the analyzed sequence.     

消失矩对小波分析求解自相似参数Hurst的影响研究

研究了消失矩对小波求解自相似参数Hurst值的影响,给出了理论推导和分析,间接论证了消失矩对Hurst参数求解的影响趋势,通过大量的实验结果给出了详细情况。理论和实验结果一致表明,在局部上,Hurst参数随着消失矩的增大呈现周期性变化,但总的趋势是随着消失矩的增大Hurst值减小,误差在增大,满足条件的最小消失矩是最佳选择。该结论能为基于自相似和小波分析的大量应用提供理论依据。     

基于小波分析的光电脉搏波奇异性处理

高精度的光电脉搏波信号,对于动态光谱法血液成分无创检测非常重要。要获得高精度的脉搏波信号,首先就要对信号中的噪声奇异点进行定位和修正。本文选择Marr小波,针对信号中的单个脉冲噪声和窄带脉冲噪声。研究了基于小波分析的光电脉搏波奇异性处理。利用脉搏波信号极大值线的周期性,在每个周期段内,首先利用单个脉冲噪声与有用信号截然不同的李氏指数特性,对单个脉冲噪声进行了处理。然后利用窄带脉冲的小波系数极大值线的特点对常规小波方法难以处理的窄带脉冲噪声进行分析定位。鉴于模极大值重构算法比较复杂,本文利用线性插值法对被定位的噪声奇异点进行了修正。仿真实验表明,利用小波分析和线性插值相结合的方法可以完成对光电脉搏波信号的奇异性处理,提高了脉搏波信号的幅值检测精度。     

A subband-based information measure of EEG during brain injury and recovery after cardiac arrest

We propose an improved quantitative measure of EEG during brain injury and recovery after cardiac arrest. In our previous studies, we proposed a measure, information quantity (IQ), to detect the early effects of temperature manipulation on the EEG signals recorded from the scalp. IQ incorporates the wavelet transform and the Shannon entropy in full bands from delta to gamma. Unlike IQ, here we separately calculate IQ in each subband, i.e., the new measure is IQ in each subband. We will call it subband IQ (SIQ). We demonstrate the performance of the proposed method by comparing SIQ with IQ in terms of how well the meausres predict actual neurological outcomes. Thirteen rats, based on 7-min cardiac arrest were used. The experimental results show that the proposed measure was more highly correlated to neurological outcome than IQ.     

周期性时间序列数据聚类算法的改进研究

聚类分析是时间数据序列分析的一种常用手段,现有的聚类算法通常从相似性度量方面进行改进.实际的时间序列数据往往具有一定的周期性和连续性,现有的算法往往忽略时间序列数据周期性和连续性特点对聚类算法的影响.对此问题进行了研究,尝试采用延拓的方法来解决该问题,从而改善聚类的效果.初步的实验结果表明了该方法的可行性和有效性.     

Evaluating coherence of natural images by smoothness membership inBesov spaces

Smoothness membership in Besov spaces B_q^α (I) is used to compare the spatial coherence of satellite images. Smoothness is given by a complexity index computed as the rate of decay of the approximation error ϵ(M) when the image is approximated by its M-largest quantized wavelet coefficient. The technique was applied to a set of nine normalized difference vegetation index (NDVI) time series data as a quantitative quality measure of spatial coherence. The NDVI data set comprises different compositing and atmospheric correction techniques. The estimates of the complexity index give a quantitative measure of the performance of these techniques that agrees well with visual evaluation and with the physics of the image collection process. The authors demonstrate the maximum value NDVI composites with Rayleigh, ozone, and water vapor correction consistently provide the highest spatial coherence among the compositing and atmospheric correction techniques evaluated. They also show the complexity index is regionally dependent and is higher in dry periods than in wet periods where residual cloud interference is more likely to appear     

提高导引头测角精度的一种新方法

分析了基于小波变换系数极大值原理的小波阈值降噪算法。由于导引头接收信号的信噪比影响了MUSIC超分辨算法的测角精度，因此提出将小波阈值降噪算法用于MUSIC超分辨算法精度分析，并进行了仿真验证，实现了改善信噪比，提高导引头测角精度的目的。     

基于小波包能量熵的混沌序列复杂度分析

混沌密码学是密码学的一个新方向,混沌序列的复杂度是衡量混沌密码学安全性的重要指标.本文将小波包能量熵应用到混沌序列的复杂度分析中,首先将混沌序列进行小波包分解,然后通过小波包能量熵计算方法确定各频段能量大小,从而确定混沌序列的复杂度.通过对Logistic、TD-ERCS和Henon产生的混沌序列进行比较分析,结果表明,小波包能量熵具有全局统计特性,无需引入新参数进行相空间重构.另外,计算方法简单,且不依赖于混沌序列的采样长度与初始值,能有效衡量混沌序列的复杂度.     

Applying a novelty filter as a matching criterion to iris recognition for binary and real-valued feature vectors

The main contributions of this paper are proposing a robust matching measure that employs multiple images of a subject to enroll an iris and that can be used with both types of feature vectors, real-valued and binary feature vectors. The first one is obtained using wavelet transforms and pixel intensity images and the second one using binary wavelet coefficients. The validation of the new matching measure proposed was done considering two utilization modes of the biometric system: verification mode and identification mode. The performance of the new matching measure is comparable to other published results. The vector with lower size was the one that uses binary wavelet coefficients, with only 10 bytes of information. Other authors reported binary feature vector sizes much greater than this one. Iris codification with vectors of lower sizes accounts for the construction of iris recognition embedded systems.